本文基于粗集理论，针对知识表达系统提出了一种新的归纳学习方法。
In the paper, we make a new inductive learning approach to knowledge representation system based on rough set theory.
复杂结构归纳学习方法按照知识表示方式不同分为基于逻辑的方法与基于数学图的方法。
The approaches adopted by inductive learning of complex structure can be classified into logic-based ones and graph-based ones according to knowledge representation.
将统计分析方法和面向属性的归纳方法结合起来，形成了一种应用面比较广的统计归纳学习方法，可以用于GIS属性数据挖掘。
A statistical inductive learning approach is proposed to investigate GIS attribute data mining. This approach integrates statistical analysis with attribute oriented induction method.
所以考生应该根据不同学科的不同特点，善于归纳总结，采用不同的学习方法，进行知识得掌握。
Therefore, candidates should be based on the different characteristics of different disciplines, good at summing up, using different learning methods, knowledge must be mastered.
考生在学习上要制定合理的计划，要根据不同学科间的特点，善于归纳总结，采用不同的学习方法进行知识的掌握。
Candidates sin the study to develop a reasonable plan, according to the characteristics of different subjects, good at summing up, using different learning methods for knowledge.
DKAS系统采用示例式学习方法，从大量经验数据归纳获取知识，知识表示为决策树形式。
By the method of learning from examples, DKAS inductively acquires knowledge, in representation of decision trees, from large amount of experience data.
总结归纳了机器学习方法在目前生物信息学的应用，并对支撑向量机（SVM）算法的基本原理做了阐述；
Secondly, summarize the application of the machine learning methods in the bioinformatics and expatiate on the rationale of the Support Vector Machine (SVM).
在归纳式学习方法中，对样本数据的不同分组会直接影响到所生成的反映变量间相互关系的规则，从而影响到对新样本的识别效果。
In inductive learning approach, how to discretize the sample data can directly affect the creation of the production rules and the recognizing result.
在归纳式学习方法中，对样本数据的不同分组会直接影响到所生成的反映变量间相互关系的规则，从而影响到对新样本的识别效果。
In inductive learning approach, how to discretize the sample data can directly affect the creation of the production rules and the recognizing result.
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